In [54]: runfile('C:/Users/INE4KOR/Desktop/Sep py/complete_model_time_seried_DF.py', wdir='C:/Users/INE4KOR/Desktop/Sep py')
What is the Future Forecast period? 5
Enter dataset: C:\Users\INE4KOR\Desktop\New_data.json
------------------------------------------------------------
Running SKU 1: Key_Sales_3...
Nan less than 60%
BEST ORDER : (1, 0, 1)
RMSE FOR lr: 0
RMSE FOR lr: 0
RMSE FOR lr: 0
RMSE FOR lr: 0
RMSE FOR lr: 0
RMSE FOR lr: 0
RMSE FOR lasso: 0
RMSE FOR lasso: 0
RMSE FOR lasso: 0
RMSE FOR lasso: 0
RMSE FOR lasso: 0
RMSE FOR lasso: 0
RMSE FOR ridge: 0
RMSE FOR ridge: 0
RMSE FOR ridge: 0
RMSE FOR ridge: 0
RMSE FOR ridge: 0
RMSE FOR ridge: 0
RMSE FOR en: 0
RMSE FOR en: 0
RMSE FOR en: 0
RMSE FOR en: 0
RMSE FOR en: 0
RMSE FOR en: 0
RMSE FOR huber: 0
RMSE FOR huber: 0
RMSE FOR huber: 0
RMSE FOR huber: 0
RMSE FOR huber: 0
RMSE FOR huber: 0
RMSE FOR llars: 0
RMSE FOR llars: 0
RMSE FOR llars: 0
RMSE FOR llars: 0
RMSE FOR llars: 0
RMSE FOR llars: 0
RMSE FOR pa: 0
RMSE FOR pa: 0
RMSE FOR pa: 0
RMSE FOR pa: 0
RMSE FOR pa: 0
RMSE FOR pa: 0
RMSE FOR cart: 0
RMSE FOR cart: 0
RMSE FOR cart: 0
RMSE FOR cart: 0
RMSE FOR cart: 0
RMSE FOR cart: 0
RMSE FOR extra: 0
RMSE FOR extra: 0
RMSE FOR extra: 0
RMSE FOR extra: 0
RMSE FOR extra: 0
RMSE FOR extra: 0
RMSE FOR svmr: 0
RMSE FOR svmr: 0
RMSE FOR svmr: 0
RMSE FOR svmr: 0
RMSE FOR svmr: 0
RMSE FOR svmr: 0
RMSE FOR ada: 0
RMSE FOR ada: 0
RMSE FOR ada: 0
RMSE FOR ada: 0
RMSE FOR ada: 0
RMSE FOR ada: 0
RMSE FOR bag: 0
RMSE FOR bag: 0
RMSE FOR bag: 0
RMSE FOR bag: 0
RMSE FOR bag: 0
RMSE FOR bag: 0
RMSE FOR rf: 0
RMSE FOR rf: 0
RMSE FOR rf: 0
RMSE FOR rf: 0
RMSE FOR rf: 0
RMSE FOR rf: 0
RMSE FOR et: 0
RMSE FOR et: 0
RMSE FOR et: 0
RMSE FOR et: 0
RMSE FOR et: 0
RMSE FOR et: 0
RMSE FOR gbm: 0
RMSE FOR gbm: 0
RMSE FOR gbm: 0
RMSE FOR gbm: 0
RMSE FOR gbm: 0
RMSE FOR gbm: 0
KEY! AR
RMSE FOR AR: 18
RMSE FOR AR: 18
RMSE FOR AR: 18
RMSE FOR AR: 18
RMSE FOR AR: 18
RMSE FOR AR: 18
KEY! MA
RMSE FOR MA: 18
RMSE FOR MA: 18
RMSE FOR MA: 18
RMSE FOR MA: 18
RMSE FOR MA: 18
RMSE FOR MA: 18
KEY! ARMA
RMSE FOR ARMA: 18
RMSE FOR ARMA: 18
RMSE FOR ARMA: 18
RMSE FOR ARMA: 18
RMSE FOR ARMA: 18
RMSE FOR ARMA: 18
KEY! ARIMA
RMSE FOR ARIMA: 18
RMSE FOR ARIMA: 18
RMSE FOR ARIMA: 18
RMSE FOR ARIMA: 18
RMSE FOR ARIMA: 18
RMSE FOR ARIMA: 18
RMSE FOR SES: 0
RMSE FOR SES: 0
RMSE FOR SES: 0
RMSE FOR SES: 0
RMSE FOR SES: 0
RMSE FOR SES: 0
RMSE FOR HWES: 0
RMSE FOR HWES: 0
RMSE FOR HWES: 0
RMSE FOR HWES: 0
RMSE FOR HWES: 0
RMSE FOR HWES: 0
RMSE FOR naive: 18
RMSE FOR naive: 18
RMSE FOR naive: 18
RMSE FOR naive_rept: 18
RMSE FOR naive_rept: 18
RMSE FOR naive_rept: 18
RMSE FOR naive3: 18
RMSE FOR naive3: 18
RMSE FOR naive3: 18
RMSE FOR naive6: 18
RMSE FOR naive6: 18
RMSE FOR naive6: 18
RMSE FOR naive12: 18
RMSE FOR naive12: 18
RMSE FOR naive12: 18
RMSE FOR naive12wa: 18
RMSE FOR naive12wa: 18
RMSE FOR naive12wa: 18
RMSE FOR sma: 18
RMSE FOR sma: 18
RMSE FOR sma: 18
RMSE FOR wma: 18
RMSE FOR wma: 18
RMSE FOR wma: 18
Moving_Average done
Modeling done
RMSE FOR Croston: 5
RMSE FOR Croston: 5
BEST MODELS : ['pa', 'SES']
ERRORS OF BEST MODELS : 0.0 0.7111112346227481
Running models for ensemble ... 0
RMSE FOR SES: 0
RMSE FOR pa: 0
Running models for ensemble ... 1
RMSE FOR SES: 0
RMSE FOR pa: 0
Running models for ensemble ... 2
RMSE FOR SES: 0
RMSE FOR pa: 0
weight ts: 1.0
weight ml: 1.0
RMSE FOR Ensemble: 0
RMSE FOR naive6wa: 18
RMSE FOR naive6wa: 18
RMSE FOR naive6wa: 18
Errors:
ML: 0.0
TS: 0.7111112346227481
Ensemble: 0.0
six_naive_WA 18.475208614068023
Best forecast from six naive
Validation Accuracy
[100.0, 100.0, 100.0, 100.0, 100.0]
Forecasts:
ML: [0, 0, 0, 0, 0]
TS: [0, 0, 0, 0, 0]
Ensemble: [0, 0, 0, 0, 0]
Best Forecast [0, 0, 0, 0, 0]
2017-07-01 00:00:00
calculate_forecast_accuracy
------------------------------------------------------------
Running SKU 2: Key_Sales_4...
Nan less than 60%
BEST ORDER : (1, 0, 1)
RMSE FOR lr: 1286
RMSE FOR lr: 1286
RMSE FOR lr: 1275
RMSE FOR lr: 1275
RMSE FOR lr: 1601
RMSE FOR lr: 1601
RMSE FOR lasso: 1286
RMSE FOR lasso: 1286
RMSE FOR lasso: 1275
RMSE FOR lasso: 1275
RMSE FOR lasso: 1601
RMSE FOR lasso: 1601
RMSE FOR ridge: 1286
RMSE FOR ridge: 1286
RMSE FOR ridge: 1275
RMSE FOR ridge: 1275
RMSE FOR ridge: 1601
RMSE FOR ridge: 1601
RMSE FOR en: 1286
RMSE FOR en: 1286
RMSE FOR en: 1275
RMSE FOR en: 1275
RMSE FOR en: 1601
RMSE FOR en: 1601
RMSE FOR huber: 1266
RMSE FOR huber: 1266
RMSE FOR huber: 811
RMSE FOR huber: 811
RMSE FOR huber: 1585
RMSE FOR huber: 1585
RMSE FOR llars: 1287
RMSE FOR llars: 1287
RMSE FOR llars: 1276
RMSE FOR llars: 1276
RMSE FOR llars: 1601
RMSE FOR llars: 1601
RMSE FOR pa: 2323
RMSE FOR pa: 2323
RMSE FOR pa: 8147
RMSE FOR pa: 8147
RMSE FOR pa: 2226
RMSE FOR pa: 2226
RMSE FOR cart: 1427
RMSE FOR cart: 1427
RMSE FOR cart: 2521
RMSE FOR cart: 2521
RMSE FOR cart: 3289
RMSE FOR cart: 3289
RMSE FOR extra: 772
RMSE FOR extra: 772
RMSE FOR extra: 2521
RMSE FOR extra: 2521
RMSE FOR extra: 3289
RMSE FOR extra: 3289
RMSE FOR svmr: 1245
RMSE FOR svmr: 1245
RMSE FOR svmr: 1276
RMSE FOR svmr: 1276
RMSE FOR svmr: 1460
RMSE FOR svmr: 1460
RMSE FOR ada: 1159
RMSE FOR ada: 1159
RMSE FOR ada: 1910
RMSE FOR ada: 1910
RMSE FOR ada: 1930
RMSE FOR ada: 1930
RMSE FOR bag: 1346
RMSE FOR bag: 1346
RMSE FOR bag: 2033
RMSE FOR bag: 2033
RMSE FOR bag: 2463
RMSE FOR bag: 2463
RMSE FOR rf: 1351
RMSE FOR rf: 1351
RMSE FOR rf: 1968
RMSE FOR rf: 1968
RMSE FOR rf: 2675
RMSE FOR rf: 2675
RMSE FOR et: 1440
RMSE FOR et: 1440
RMSE FOR et: 2217
RMSE FOR et: 2217
RMSE FOR et: 3196
RMSE FOR et: 3196
RMSE FOR gbm: 1353
RMSE FOR gbm: 1353
RMSE FOR gbm: 3416
RMSE FOR gbm: 3416
RMSE FOR gbm: 2682
RMSE FOR gbm: 2682
KEY! AR
RMSE FOR AR: 2802
RMSE FOR AR: 2802
RMSE FOR AR: 3103
RMSE FOR AR: 3103
RMSE FOR AR: 2355
RMSE FOR AR: 2355
KEY! MA
RMSE FOR MA: 2516
RMSE FOR MA: 2516
RMSE FOR MA: 2483
RMSE FOR MA: 2483
RMSE FOR MA: 2844
RMSE FOR MA: 2844
KEY! ARMA
RMSE FOR ARMA: 3099
RMSE FOR ARMA: 3099
RMSE FOR ARMA: 2943
RMSE FOR ARMA: 2943
RMSE FOR ARMA: 1777
RMSE FOR ARMA: 1777
KEY! ARIMA
RMSE FOR ARIMA: 2516
RMSE FOR ARIMA: 2516
RMSE FOR ARIMA: 2809
RMSE FOR ARIMA: 2809
RMSE FOR ARIMA: 2844
RMSE FOR ARIMA: 2844
RMSE FOR SES: 1298
RMSE FOR SES: 1298
RMSE FOR SES: 1317
RMSE FOR SES: 1317
RMSE FOR SES: 1559
RMSE FOR SES: 1559
RMSE FOR HWES: 1298
RMSE FOR HWES: 1298
RMSE FOR HWES: 1317
RMSE FOR HWES: 1317
RMSE FOR HWES: 1559
RMSE FOR HWES: 1559
RMSE FOR naive: 1818
RMSE FOR naive: 2630
RMSE FOR naive: 4623
RMSE FOR naive_rept: 2216
RMSE FOR naive_rept: 2760
RMSE FOR naive_rept: 3679
RMSE FOR naive3: 3144
RMSE FOR naive3: 2931
RMSE FOR naive3: 3128
RMSE FOR naive6: 3371
RMSE FOR naive6: 3409
RMSE FOR naive6: 1248
RMSE FOR naive12: 2404
RMSE FOR naive12: 2484
RMSE FOR naive12: 1442
RMSE FOR naive12wa: 2953
RMSE FOR naive12wa: 2950
RMSE FOR naive12wa: 1179
RMSE FOR sma: 2516
RMSE FOR sma: 2483
RMSE FOR sma: 2844
RMSE FOR wma: 2410
RMSE FOR wma: 2452
RMSE FOR wma: 2942
Moving_Average done
Modeling done
RMSE FOR Croston: 1818
RMSE FOR Croston: 1818
BEST MODELS : ['huber', 'SES']
ERRORS OF BEST MODELS : 1221.3052514664994 1391.7984033715213
Running models for ensemble ... 0
RMSE FOR SES: 1324
RMSE FOR huber: 1309
Running models for ensemble ... 1
RMSE FOR SES: 1341
RMSE FOR huber: 2043
Running models for ensemble ... 2
RMSE FOR SES: 1614
RMSE FOR huber: 1542
weight ts: 0.5299204103711289
weight ml: 0.47007958962887103
RMSE FOR Ensemble: 1258
RMSE FOR naive6wa: 3175
RMSE FOR naive6wa: 3133
RMSE FOR naive6wa: 798
Errors:
ML: 1221.3052514664994
TS: 1391.7984033715213
Ensemble: 1258.741593815029
six_naive_WA 2369.1142395023844
Best forecast from six naive
Validation Accuracy
[67.203, 54.842, 0, 43.492, 35.084]
Forecasts:
ML: [2915, 2534, 2822, 2748, 2804]
TS: [2944, 2944, 2944, 2944, 2944]
Ensemble: [2930, 2751, 2886, 2851, 2878]
Best Forecast [1738, 2887, 3086, 1236, 3121]
2017-07-01 00:00:00
calculate_forecast_accuracy
------------------------------------------------------------
Running SKU 3: Key_Sales_5...
Nan less than 60%
BEST ORDER : (1, 0, 0)
RMSE FOR lr: 280
RMSE FOR lr: 280
RMSE FOR lr: 231
RMSE FOR lr: 231
RMSE FOR lr: 304
RMSE FOR lr: 304
RMSE FOR lasso: 280
RMSE FOR lasso: 280
RMSE FOR lasso: 231
RMSE FOR lasso: 231
RMSE FOR lasso: 304
RMSE FOR lasso: 304
RMSE FOR ridge: 280
RMSE FOR ridge: 280
RMSE FOR ridge: 231
RMSE FOR ridge: 231
RMSE FOR ridge: 304
RMSE FOR ridge: 304
RMSE FOR en: 280
RMSE FOR en: 280
RMSE FOR en: 231
RMSE FOR en: 231
RMSE FOR en: 304
RMSE FOR en: 304
RMSE FOR huber: 279
RMSE FOR huber: 279
RMSE FOR huber: 223
RMSE FOR huber: 223
RMSE FOR huber: 350
RMSE FOR huber: 350
RMSE FOR llars: 279
RMSE FOR llars: 279
RMSE FOR llars: 232
RMSE FOR llars: 232
RMSE FOR llars: 298
RMSE FOR llars: 298
RMSE FOR pa: 1627
RMSE FOR pa: 1627
RMSE FOR pa: 238
RMSE FOR pa: 238
RMSE FOR pa: 1591
RMSE FOR pa: 1591
RMSE FOR cart: 385
RMSE FOR cart: 385
RMSE FOR cart: 304
RMSE FOR cart: 304
RMSE FOR cart: 683
RMSE FOR cart: 683
RMSE FOR extra: 385
RMSE FOR extra: 385
RMSE FOR extra: 304
RMSE FOR extra: 304
RMSE FOR extra: 628
RMSE FOR extra: 628
RMSE FOR svmr: 285
RMSE FOR svmr: 285
RMSE FOR svmr: 266
RMSE FOR svmr: 266
RMSE FOR svmr: 168
RMSE FOR svmr: 168
RMSE FOR ada: 269
RMSE FOR ada: 269
RMSE FOR ada: 283
RMSE FOR ada: 283
RMSE FOR ada: 525
RMSE FOR ada: 525
RMSE FOR bag: 275
RMSE FOR bag: 275
RMSE FOR bag: 191
RMSE FOR bag: 191
RMSE FOR bag: 552
RMSE FOR bag: 552
RMSE FOR rf: 392
RMSE FOR rf: 392
RMSE FOR rf: 241
RMSE FOR rf: 241
RMSE FOR rf: 574
RMSE FOR rf: 574
RMSE FOR et: 272
RMSE FOR et: 272
RMSE FOR et: 304
RMSE FOR et: 304
RMSE FOR et: 480
RMSE FOR et: 480
RMSE FOR gbm: 305
RMSE FOR gbm: 305
RMSE FOR gbm: 496
RMSE FOR gbm: 496
RMSE FOR gbm: 539
RMSE FOR gbm: 539
KEY! AR
RMSE FOR AR: 245
RMSE FOR AR: 245
RMSE FOR AR: 118
RMSE FOR AR: 118
RMSE FOR AR: 200
RMSE FOR AR: 200
KEY! MA
RMSE FOR MA: 346
RMSE FOR MA: 346
RMSE FOR MA: 301
RMSE FOR MA: 301
RMSE FOR MA: 460
RMSE FOR MA: 460
KEY! ARMA
RMSE FOR ARMA: 245
RMSE FOR ARMA: 245
RMSE FOR ARMA: 118
RMSE FOR ARMA: 118
RMSE FOR ARMA: 200
RMSE FOR ARMA: 200
KEY! ARIMA
RMSE FOR ARIMA: 200
RMSE FOR ARIMA: 200
RMSE FOR ARIMA: 358
RMSE FOR ARIMA: 358
RMSE FOR ARIMA: 459
RMSE FOR ARIMA: 459
RMSE FOR SES: 338
RMSE FOR SES: 338
RMSE FOR SES: 318
RMSE FOR SES: 318
RMSE FOR SES: 84
RMSE FOR SES: 84
RMSE FOR HWES: 338
RMSE FOR HWES: 338
RMSE FOR HWES: 318
RMSE FOR HWES: 318
RMSE FOR HWES: 84
RMSE FOR HWES: 84
RMSE FOR naive: 203
RMSE FOR naive: 376
RMSE FOR naive: 456
RMSE FOR naive_rept: 246
RMSE FOR naive_rept: 365
RMSE FOR naive_rept: 491
RMSE FOR naive3: 567
RMSE FOR naive3: 453
RMSE FOR naive3: 456
RMSE FOR naive6: 536
RMSE FOR naive6: 297
RMSE FOR naive6: 225
RMSE FOR naive12: 521
RMSE FOR naive12: 399
RMSE FOR naive12: 237
RMSE FOR naive12wa: 598
RMSE FOR naive12wa: 383
RMSE FOR naive12wa: 119
RMSE FOR sma: 346
RMSE FOR sma: 301
RMSE FOR sma: 460
RMSE FOR wma: 314
RMSE FOR wma: 302
RMSE FOR wma: 462
Moving_Average done
Modeling done
RMSE FOR Croston: 358
RMSE FOR Croston: 358
BEST MODELS : ['svmr', 'AR']
ERRORS OF BEST MODELS : 240.33489952196865 188.33671607404844
Running models for ensemble ... 0
RMSE FOR AR: 359
RMSE FOR svmr: 285
Running models for ensemble ... 1
RMSE FOR AR: 252
RMSE FOR svmr: 266
Running models for ensemble ... 2
RMSE FOR AR: 513
RMSE FOR svmr: 168
weight ts: 0.40119770215377243
weight ml: 0.5988022978462276
RMSE FOR Ensemble: 219
RMSE FOR naive6wa: 481
RMSE FOR naive6wa: 208
RMSE FOR naive6wa: 107
Errors:
ML: 240.33489952196865
TS: 188.33671607404844
Ensemble: 219.69569863791142
six_naive_WA 265.99243310191343
Best forecast from TS
Validation Accuracy
[87.705, 87.879, 6.926, 72.772, 38.889]
Forecasts:
ML: [603, 602, 602, 602, 602]
TS: [435, 412, 398, 382, 367]
Ensemble: [535, 525, 520, 513, 507]
Best Forecast [435, 412, 398, 382, 367]
2017-07-01 00:00:00
calculate_forecast_accuracy
------------------------------------------------------------
Running SKU 4: Key_Sales_6...
Nan less than 60%
BEST ORDER : (12, 0, 0)
RMSE FOR lr: 101
RMSE FOR lr: 101
RMSE FOR lr: 97
RMSE FOR lr: 97
RMSE FOR lr: 87
RMSE FOR lr: 87
RMSE FOR lasso: 101
RMSE FOR lasso: 101
RMSE FOR lasso: 97
RMSE FOR lasso: 97
RMSE FOR lasso: 87
RMSE FOR lasso: 87
RMSE FOR ridge: 101
RMSE FOR ridge: 101
RMSE FOR ridge: 97
RMSE FOR ridge: 97
RMSE FOR ridge: 87
RMSE FOR ridge: 87
RMSE FOR en: 101
RMSE FOR en: 101
RMSE FOR en: 97
RMSE FOR en: 97
RMSE FOR en: 87
RMSE FOR en: 87
RMSE FOR huber: 47
RMSE FOR huber: 47
RMSE FOR huber: 47
RMSE FOR huber: 47
RMSE FOR huber: 54
RMSE FOR huber: 54
RMSE FOR llars: 95
RMSE FOR llars: 95
RMSE FOR llars: 101
RMSE FOR llars: 101
RMSE FOR llars: 92
RMSE FOR llars: 92
RMSE FOR pa: 108
RMSE FOR pa: 108
RMSE FOR pa: 69
RMSE FOR pa: 69
RMSE FOR pa: 73
RMSE FOR pa: 73
RMSE FOR cart: 50
RMSE FOR cart: 50
RMSE FOR cart: 111
RMSE FOR cart: 111
RMSE FOR cart: 214
RMSE FOR cart: 214
RMSE FOR extra: 46
RMSE FOR extra: 46
RMSE FOR extra: 77
RMSE FOR extra: 77
RMSE FOR extra: 79
RMSE FOR extra: 79
RMSE FOR svmr: 82
RMSE FOR svmr: 82
RMSE FOR svmr: 110
RMSE FOR svmr: 110
RMSE FOR svmr: 124
RMSE FOR svmr: 124
RMSE FOR ada: 75
RMSE FOR ada: 75
RMSE FOR ada: 97
RMSE FOR ada: 97
RMSE FOR ada: 97
RMSE FOR ada: 97
RMSE FOR bag: 83
RMSE FOR bag: 83
RMSE FOR bag: 107
RMSE FOR bag: 107
RMSE FOR bag: 104
RMSE FOR bag: 104
RMSE FOR rf: 89
RMSE FOR rf: 89
RMSE FOR rf: 96
RMSE FOR rf: 96
RMSE FOR rf: 113
RMSE FOR rf: 113
RMSE FOR et: 86
RMSE FOR et: 86
RMSE FOR et: 122
RMSE FOR et: 122
RMSE FOR et: 115
RMSE FOR et: 115
RMSE FOR gbm: 95
RMSE FOR gbm: 95
RMSE FOR gbm: 113
RMSE FOR gbm: 113
RMSE FOR gbm: 126
RMSE FOR gbm: 126
KEY! AR
RMSE FOR AR: 77
RMSE FOR AR: 77
RMSE FOR AR: 102
RMSE FOR AR: 102
RMSE FOR AR: 83
RMSE FOR AR: 83
KEY! MA
RMSE FOR MA: 77
RMSE FOR MA: 77
RMSE FOR MA: 102
RMSE FOR MA: 102
RMSE FOR MA: 70
RMSE FOR MA: 70
KEY! ARMA
RMSE FOR ARMA: 77
RMSE FOR ARMA: 77
RMSE FOR ARMA: 102
RMSE FOR ARMA: 102
RMSE FOR ARMA: 83
RMSE FOR ARMA: 83
KEY! ARIMA
RMSE FOR ARIMA: 76
RMSE FOR ARIMA: 76
RMSE FOR ARIMA: 102
RMSE FOR ARIMA: 102
RMSE FOR ARIMA: 70
RMSE FOR ARIMA: 70
RMSE FOR SES: 41
RMSE FOR SES: 41
RMSE FOR SES: 55
RMSE FOR SES: 55
RMSE FOR SES: 58
RMSE FOR SES: 58
RMSE FOR HWES: 41
RMSE FOR HWES: 41
RMSE FOR HWES: 55
RMSE FOR HWES: 55
RMSE FOR HWES: 58
RMSE FOR HWES: 58
RMSE FOR naive: 49
RMSE FOR naive: 71
RMSE FOR naive: 70
RMSE FOR naive_rept: 61
RMSE FOR naive_rept: 66
RMSE FOR naive_rept: 77
RMSE FOR naive3: 126
RMSE FOR naive3: 100
RMSE FOR naive3: 94
RMSE FOR naive6: 63
RMSE FOR naive6: 59
RMSE FOR naive6: 80
RMSE FOR naive12: 49
RMSE FOR naive12: 71
RMSE FOR naive12: 70
RMSE FOR naive12wa: 68
RMSE FOR naive12wa: 78
RMSE FOR naive12wa: 78
RMSE FOR sma: 77
RMSE FOR sma: 102
RMSE FOR sma: 70
RMSE FOR wma: 76
RMSE FOR wma: 103
RMSE FOR wma: 65
Moving_Average done
Modeling done
RMSE FOR Croston: 76
RMSE FOR Croston: 76
BEST MODELS : ['huber', 'SES']
ERRORS OF BEST MODELS : 50.14984763013127 51.638453657303614
Running models for ensemble ... 0
RMSE FOR SES: 89
RMSE FOR huber: 52
Running models for ensemble ... 1
RMSE FOR SES: 117
RMSE FOR huber: 73
Running models for ensemble ... 2
RMSE FOR SES: 131
RMSE FOR huber: 74
weight ts: 0.3727648397373213
weight ml: 0.6272351602626788
RMSE FOR Ensemble: 41
RMSE FOR naive6wa: 50
RMSE FOR naive6wa: 41
RMSE FOR naive6wa: 55
Errors:
ML: 50.14984763013127
TS: 51.638453657303614
Ensemble: 41.54756310543375
six_naive_WA 48.90675665047154
Best forecast from six naive
Validation Accuracy
[83.871, 80.556, 68.519, 62.179, 32.222]
Forecasts:
ML: [180, 250, 245, 305, 247]
TS: [223, 223, 223, 223, 223]
Ensemble: [196, 239, 236, 274, 238]
Best Forecast [214, 232, 242, 179, 170]
2017-07-01 00:00:00
calculate_forecast_accuracy
------------------------------------------------------------
Running SKU 5: Key_Sales_7...
Nan less than 60%
BEST ORDER : (1, 1, 0)
RMSE FOR lr: 1985
RMSE FOR lr: 1985
RMSE FOR lr: 2016
RMSE FOR lr: 2016
RMSE FOR lr: 1861
RMSE FOR lr: 1861
RMSE FOR lasso: 1985
RMSE FOR lasso: 1985
RMSE FOR lasso: 2016
RMSE FOR lasso: 2016
RMSE FOR lasso: 1861
RMSE FOR lasso: 1861
RMSE FOR ridge: 1985
RMSE FOR ridge: 1985
RMSE FOR ridge: 2016
RMSE FOR ridge: 2016
RMSE FOR ridge: 1861
RMSE FOR ridge: 1861
RMSE FOR en: 1985
RMSE FOR en: 1985
RMSE FOR en: 2016
RMSE FOR en: 2016
RMSE FOR en: 1861
RMSE FOR en: 1861
RMSE FOR huber: 1359
RMSE FOR huber: 1359
RMSE FOR huber: 1195
RMSE FOR huber: 1195
RMSE FOR huber: 1225
RMSE FOR huber: 1225
RMSE FOR llars: 1990
RMSE FOR llars: 1990
RMSE FOR llars: 2021
RMSE FOR llars: 2021
RMSE FOR llars: 1866
RMSE FOR llars: 1866
RMSE FOR pa: 327
RMSE FOR pa: 327
RMSE FOR pa: 260
RMSE FOR pa: 260
RMSE FOR pa: 167
RMSE FOR pa: 167
RMSE FOR cart: 204
RMSE FOR cart: 204
RMSE FOR cart: 1923
RMSE FOR cart: 1923
RMSE FOR cart: 310
RMSE FOR cart: 310
RMSE FOR extra: 204
RMSE FOR extra: 204
RMSE FOR extra: 1923
RMSE FOR extra: 1923
RMSE FOR extra: 310
RMSE FOR extra: 310
RMSE FOR svmr: 2248
RMSE FOR svmr: 2248
RMSE FOR svmr: 2275
RMSE FOR svmr: 2275
RMSE FOR svmr: 2210
RMSE FOR svmr: 2210
RMSE FOR ada: 1129
RMSE FOR ada: 1129
RMSE FOR ada: 1779
RMSE FOR ada: 1779
RMSE FOR ada: 1457
RMSE FOR ada: 1457
RMSE FOR bag: 595
RMSE FOR bag: 595
RMSE FOR bag: 1153
RMSE FOR bag: 1153
RMSE FOR bag: 425
RMSE FOR bag: 425
RMSE FOR rf: 542
RMSE FOR rf: 542
RMSE FOR rf: 1155
RMSE FOR rf: 1155
RMSE FOR rf: 488
RMSE FOR rf: 488
RMSE FOR et: 267
RMSE FOR et: 267
RMSE FOR et: 1786
RMSE FOR et: 1786
RMSE FOR et: 493
RMSE FOR et: 493
RMSE FOR gbm: 176
RMSE FOR gbm: 176
RMSE FOR gbm: 1760
RMSE FOR gbm: 1760
RMSE FOR gbm: 234
RMSE FOR gbm: 234
KEY! AR
RMSE FOR AR: 347
RMSE FOR AR: 347
RMSE FOR AR: 247
RMSE FOR AR: 247
RMSE FOR AR: 57
RMSE FOR AR: 57
KEY! MA
RMSE FOR MA: 1835
RMSE FOR MA: 1835
RMSE FOR MA: 888
RMSE FOR MA: 888
RMSE FOR MA: 421
RMSE FOR MA: 421
KEY! ARMA
RMSE FOR ARMA: 347
RMSE FOR ARMA: 347
RMSE FOR ARMA: 247
RMSE FOR ARMA: 247
RMSE FOR ARMA: 57
RMSE FOR ARMA: 57
KEY! ARIMA
RMSE FOR ARIMA: 859
RMSE FOR ARIMA: 859
RMSE FOR ARIMA: 165
RMSE FOR ARIMA: 165
RMSE FOR ARIMA: 90
RMSE FOR ARIMA: 90
RMSE FOR SES: 195
RMSE FOR SES: 195
RMSE FOR SES: 183
RMSE FOR SES: 183
RMSE FOR SES: 182
RMSE FOR SES: 182
RMSE FOR HWES: 195
RMSE FOR HWES: 195
RMSE FOR HWES: 183
RMSE FOR HWES: 183
RMSE FOR HWES: 182
RMSE FOR HWES: 182
RMSE FOR naive: 998
RMSE FOR naive: 168
RMSE FOR naive: 338
RMSE FOR naive_rept: 11220
RMSE FOR naive_rept: 11286
RMSE FOR naive_rept: 11407
RMSE FOR naive3: 2116
RMSE FOR naive3: 1532
RMSE FOR naive3: 655
RMSE FOR naive6: 1153
RMSE FOR naive6: 1949
RMSE FOR naive6: 2408
RMSE FOR naive12: 1149
RMSE FOR naive12: 1355
RMSE FOR naive12: 1310
RMSE FOR naive12wa: 2514
RMSE FOR naive12wa: 2389
RMSE FOR naive12wa: 2246
RMSE FOR sma: 1835
RMSE FOR sma: 888
RMSE FOR sma: 421
RMSE FOR wma: 1768
RMSE FOR wma: 777
RMSE FOR wma: 390
Moving_Average done
Modeling done
RMSE FOR Croston: 2899
RMSE FOR Croston: 2899
BEST MODELS : ['pa', 'SES']
ERRORS OF BEST MODELS : 251.99842237864223 187.175826149877
Running models for ensemble ... 0
RMSE FOR SES: 332
RMSE FOR pa: 409
Running models for ensemble ... 1
RMSE FOR SES: 336
RMSE FOR pa: 319
Running models for ensemble ... 2
RMSE FOR SES: 253
RMSE FOR pa: 2707
weight ts: 0.6513566187092448
weight ml: 0.3486433812907552
RMSE FOR Ensemble: 169
RMSE FOR naive6wa: 1063
RMSE FOR naive6wa: 1703
RMSE FOR naive6wa: 2034
Errors:
ML: 251.99842237864223
TS: 187.175826149877
Ensemble: 169.10824935525764
six_naive_WA 1600.518324471727
Best forecast from six naive
Validation Accuracy
[76.42, 0, 0, 0, 0]
Forecasts:
ML: [279, 104, 0, 0, 0]
TS: [457, 457, 457, 457, 457]
Ensemble: [394, 333, 297, 297, 297]
Best Forecast [299, 536, 404, 218, 403]
2017-07-01 00:00:00
calculate_forecast_accuracy
In [55]: